{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T21:15:15Z","timestamp":1789247715703,"version":"build-2803163510"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,12,6]],"date-time":"2019-12-06T00:00:00Z","timestamp":1575590400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,12,6]],"date-time":"2019-12-06T00:00:00Z","timestamp":1575590400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Nucl Med Mol Imaging"],"published-print":{"date-parts":[[2020,2]]},"DOI":"10.1007\/s00259-019-04595-y","type":"journal-article","created":{"date-parts":[[2019,12,6]],"date-time":"2019-12-06T09:02:57Z","timestamp":1575622977000},"page":"332-341","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases"],"prefix":"10.1007","volume":"47","author":[{"given":"Hye Joo","family":"Son","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jungsu S.","family":"Oh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minyoung","family":"Oh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soo Jong","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae-Hong","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jee Hoon","family":"Roh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Seung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,12,6]]},"reference":[{"key":"4595_CR1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1097\/WAD.0b013e318279d02a","volume":"27","author":"M Grundman","year":"2013","unstructured":"Grundman M, Pontecorvo MJ, Salloway SP, Doraiswamy PM, Fleisher AS, Sadowsky CH, et al. Potential impact of amyloid imaging on diagnosis and intended management in patients with progressive cognitive decline. Alzheimer Dis Assoc Disord. 2013;27:4\u201315.","journal-title":"Alzheimer Dis Assoc Disord"},{"key":"4595_CR2","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1007\/s12149-014-0924-8","volume":"29","author":"C Hosokawa","year":"2015","unstructured":"Hosokawa C, Ishii K, Hyodo T, Sakaguchi K, Usami K, Shimamoto K, et al. Investigation of (11)C-PiB equivocal PET findings. Ann Nucl Med. 2015;29:164\u20139.","journal-title":"Ann Nucl Med"},{"key":"4595_CR3","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1001\/jamaneurol.2015.1633","volume":"72","author":"S Schreiber","year":"2015","unstructured":"Schreiber S, Landau SM, Fero A, Schreiber F, Jagust WJ. Alzheimer\u2019s Disease Neuroimaging Initiative. Comparison of visual and quantitative florbetapir F 18 positron emission tomography analysis in predicting mild cognitive impairment outcomes. JAMA Neurol. 2015;72:1183\u201390.","journal-title":"JAMA Neurol"},{"key":"4595_CR4","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86:2278\u2013324.","journal-title":"Proc IEEE"},{"key":"4595_CR5","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436\u201344.","journal-title":"Nature."},{"key":"4595_CR6","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402\u201310.","journal-title":"JAMA."},{"key":"4595_CR7","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1002\/hbm.22254","volume":"35","author":"M Liu","year":"2014","unstructured":"Liu M, Zhang D, Shen D. Alzheimer\u2019s Disease Neuroimaging Initiative. Hierarchical fusion of features and classifier decisions for Alzheimer\u2019s disease diagnosis. Hum Brain Mapp. 2014;35:1305\u201319.","journal-title":"Hum Brain Mapp"},{"key":"4595_CR8","unstructured":"Sarraf S, Tofighi G. Classification of Alzheimer\u2019s disease structural MRI data by deep learning convolutional neural networks. 2017. https:\/\/arxiv.org\/abs\/1607.06583. Accessed 5 Apr 2019."},{"key":"4595_CR9","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3389\/fninf.2018.00023","volume":"12","author":"D Wen","year":"2018","unstructured":"Wen D, Wei ZH, Zhou YH, Li GL, Zhang X, Han W. Deep learning methods to process fMRI data and their application in the diagnosis of cognitive impairment: a brief overview and our opinion. Front Neuroinform. 2018;12:23.","journal-title":"Front Neuroinform"},{"key":"4595_CR10","doi-asserted-by":"crossref","unstructured":"Singh S, Srivastava A, Mi L, Caselli RJ, Chen K, Goradia D, Reiman EM, Wang Y. Deep-learning-based classification of FDG-PET data for Alzheimer\u2019s disease categories. In: 13th international conference on medical information processing and analysis. San Andres Islands, Colombia: International Society for Optics and Photonics; 2017. pp. 105720J.","DOI":"10.1117\/12.2294537"},{"key":"4595_CR11","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.2967\/jnumed.116.174615","volume":"57","author":"S Minoshima","year":"2016","unstructured":"Minoshima S, Drzezga AE, Barthel H, Bohnen N, Djekidel M, Lewis DH, et al. SNMMI procedure standard\/EANM practice guideline for amyloid PET imaging of the brain 1.0. J Nucl Med. 2016;57:1316\u201322.","journal-title":"J Nucl Med"},{"key":"4595_CR12","unstructured":"NeuraCeq. NEURACEQ (florbetaben F 18 injection), highlights of prescribing information. 2017. http:\/\/piramal.com\/neuraceq\/images\/Neuraceq_PI.pdf. Accessed 5 Apr 2019."},{"key":"4595_CR13","first-page":"2579","volume":"9","author":"LD Van Maaten","year":"2008","unstructured":"Van Maaten LD, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9:2579\u2013605.","journal-title":"J Mach Learn Res"},{"key":"4595_CR14","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1016\/j.neuroimage.2012.09.015","volume":"64","author":"R Vandenberghe","year":"2013","unstructured":"Vandenberghe R, Nelissen N, Salmon E, Ivanoiu A, Hasselbalch S, Andersen A, et al. Binary classification of 18F-flutemetamol PET using machine learning: comparison with visual reads and structural MRI. Neuroimage. 2013;64:517\u201325.","journal-title":"Neuroimage."},{"key":"4595_CR15","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1148\/radiol.2018180958","volume":"290","author":"Y Ding","year":"2019","unstructured":"Ding Y, Sohn JH, Kawczynski MG, Trivedi H, Harnish R, Jenkins NW, et al. A deep learning model to predict a diagnosis of Alzheimer disease by using (18)F-FDG PET of the brain. Radiology. 2019;290:456\u201364.","journal-title":"Radiology."},{"key":"4595_CR16","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1007\/s00259-015-3067-9","volume":"42","author":"P Payoux","year":"2015","unstructured":"Payoux P, Delrieu J, Gallini A, Adel D, Salabert AS, Hitzel A, et al. Cognitive and functional patterns of nondemented subjects with equivocal visual amyloid PET findings. Eur J Nucl Med Mol Imaging. 2015;42:1459\u201368.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"4595_CR17","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1016\/j.nicl.2016.05.004","volume":"12","author":"L Cattell","year":"2016","unstructured":"Cattell L, Platsch G, Pfeiffer R, Declerck J, Schnabel JA, Hutton C. Alzheimer\u2019s Disease Neuroimaging Initiative. Classification of amyloid status using machine learning with histograms of oriented 3D gradients. Neuroimage Clin. 2016;12:990\u20131003.","journal-title":"Neuroimage Clin"},{"key":"4595_CR18","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.neuroimage.2013.01.015","volume":"71","author":"AD Cohen","year":"2013","unstructured":"Cohen AD, Mowrey W, Weissfeld LA, Aizenstein HJ, McDade E, Mountz JM, et al. Classification of amyloid-positivity in controls: comparison of visual read and quantitative approaches. Neuroimage. 2013;71:207\u201315.","journal-title":"Neuroimage."},{"key":"4595_CR19","doi-asserted-by":"publisher","first-page":"P315","DOI":"10.1016\/j.jalz.2018.06.121","volume":"14","author":"Y Yuan","year":"2018","unstructured":"Yuan Y, Wang Z, Lee W, VanGilder P, Chen Y, Reiman EM, et al. Quantification of amyloid burden from florbetapir pet images without using target and reference regions: preliminary findings based on the deep learning 3D convolutional neural network approach. Alzheimers Dement. 2018;14:P315\u20136.","journal-title":"Alzheimers Dement"},{"issue":"17","key":"4595_CR20","doi-asserted-by":"publisher","first-page":"e1452","DOI":"10.1212\/WNL.0000000000005354","volume":"90","author":"SM Landau","year":"2018","unstructured":"Landau SM, Horng A, Jagust WJ. Initiative AsDN. Memory decline accompanies subthreshold amyloid accumulation. Neurology. 2018;90(17):e1452\u2013e60.","journal-title":"Neurology."},{"issue":"2","key":"4595_CR21","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.jalz.2018.09.001","volume":"15","author":"R La Joie","year":"2019","unstructured":"La Joie R, Ayakta N, Seeley WW, Borys E, Boxer AL, DeCarli C, et al. Multisite study of the relationships between antemortem [11C] PIB-PET centiloid values and postmortem measures of Alzheimer\u2019s disease neuropathology. Alzheimers Dement. 2019;15(2):205\u201316.","journal-title":"Alzheimers Dement"},{"key":"4595_CR22","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.bbr.2018.02.017","volume":"344","author":"H Choi","year":"2018","unstructured":"Choi H, Jin KH. Alzheimer\u2019s Disease Neuroimaging Initiative. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res. 2018;344:103\u20139.","journal-title":"Behav Brain Res"},{"key":"4595_CR23","doi-asserted-by":"publisher","first-page":"6763","DOI":"10.1038\/s41598-017-06624-y","volume":"7","author":"D Gamberger","year":"2017","unstructured":"Gamberger D, Lavra\u010d N, Srivatsa S, Tanzi RE, Doraiswamy PM. Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer\u2019s disease. Sci Rep. 2017;7:6763.","journal-title":"Sci Rep"},{"key":"4595_CR24","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1016\/S1474-4422(10)70119-8","volume":"9","author":"F Mangialasche","year":"2010","unstructured":"Mangialasche F, Solomon A, Winblad B, Mecocci P, Kivipelto M. Alzheimer\u2019s disease: clinical trials and drug development. Lancet Neurol. 2010;9:702\u201316.","journal-title":"Lancet Neurol"},{"key":"4595_CR25","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/alzrt68","volume":"3","author":"R Spiegel","year":"2011","unstructured":"Spiegel R, Berres M, Miserez AR, Monsch AU, Alzheimer\u2019s Disease Neuroimaging Initiative. For debate: substituting placebo controls in long-term Alzheimer\u2019s prevention trials. Alzheimers Res Ther. 2011;3:9.","journal-title":"Alzheimers Res Ther"},{"key":"4595_CR26","unstructured":"Payan A, Montana G. Predicting Alzheimer\u2019s disease: a neuroimaging study with 3D convolutional neural networks. 2015. https:\/\/arxiv.org\/abs\/1502.02506. Accessed 5 Apr 2019."},{"key":"4595_CR27","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/978-3-030-00928-1_58","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Ke Yan","year":"2018","unstructured":"Yan K, Bagheri M, Summers RM. 3D context enhanced region-based convolutional neural network for end-to-end lesion detection. In: 21th international conference on medical image computing and computer assisted intervention. Granada, Spain: Springer; 2018. pp. 511\u20139."},{"key":"4595_CR28","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.neuroimage.2018.03.065","volume":"175","author":"G Zhao","year":"2018","unstructured":"Zhao G, Liu F, Oler JA, Meyerand ME, Kalin NH, Birn RM. Bayesian convolutional neural network based MRI brain extraction on nonhuman primates. Neuroimage. 2018;175:32\u201344.","journal-title":"Neuroimage."},{"key":"4595_CR29","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1002\/ana.24454","volume":"78","author":"BW Patterson","year":"2015","unstructured":"Patterson BW, Elbert DL, Mawuenyega KG, Kasten T, Ovod V, Ma S, et al. Age and amyloid effects on human central nervous system amyloid-beta kinetics. Ann Neurol. 2015;78:439\u201353.","journal-title":"Ann Neurol"}],"container-title":["European Journal of Nuclear Medicine and Molecular Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00259-019-04595-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00259-019-04595-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00259-019-04595-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,5]],"date-time":"2020-12-05T00:18:54Z","timestamp":1607127534000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00259-019-04595-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,6]]},"references-count":29,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,2]]}},"alternative-id":["4595"],"URL":"https:\/\/doi.org\/10.1007\/s00259-019-04595-y","relation":{},"ISSN":["1619-7070","1619-7089"],"issn-type":[{"value":"1619-7070","type":"print"},{"value":"1619-7089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,6]]},"assertion":[{"value":"16 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standard"}},{"value":"The authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Informed written consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and the principles of the 1964 Declaration of Helsinki and its subsequent amendments or comparable ethical standards.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}